First‐line immunotherapy of metastatic renal cell carcinoma: an updated network meta‐analysis including triplet therapy
Bibliographic record
Abstract
OBJECTIVE: To compare the differential efficacy of first-line immune checkpoint inhibitor (ICI)-based combined therapies among patients with intermediate- and poor-risk metastatic renal cell carcinoma (mRCC), as recently, the efficacy of triplet therapy comprising nivolumab plus ipilimumab plus cabozantinib has been published. PATIENTS AND METHODS: Three databases were searched in December 2022 for randomised controlled trials (RCTs) analysing oncological outcomes in patients with mRCC treated with first-line ICI-based combined therapies. We performed network meta-analysis (NMA) to compare the outcomes, including progression-free survival (PFS) and objective response rates (ORRs), in patients with intermediate- and poor-risk mRCC; we also assessed treatment-related adverse events. RESULTS: Overall, seven RCTs were included in the meta-analyses and NMAs. Treatment ranking analysis revealed that pembrolizumab + lenvatinib (99%) had the highest likelihood of improved PFS, followed by nivolumab + cabozantinib (79%), and nivolumab + ipilimumab + cabozantinib (77%). Notably, compared to nivolumab + cabozantinib, adding ipilimumab to nivolumab + cabozantinib did not improve PFS (hazard ratio 1.02, 95% confidence interval 0.72-1.43). Regarding ORRs, treatment ranking analysis also revealed that pembrolizumab + lenvatinib had the highest likelihood of providing better ORRs (99.7%). The likelihoods of improved PFS and ORRs of pembrolizumab + lenvatinib were true in both International Metastatic RCC Database Consortium (IMDC) risk groups. CONCLUSIONS: Our analyses confirmed the robust efficacy of pembrolizumab + lenvatinib as first-line treatment for patients with intermediate or poor IMDC risk mRCC. Triplet therapy did not result in superior efficacy. Considering both toxicity and the lack of mature overall survival data, triplet therapy should only be considered in selected patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.049 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".